Building Applications with Vector Databases

Free online courses from Neo4j GraphAcademy teaching how to build RAG systems on knowledge graphs. Covers fundamentals of combining graph databases with vector search for more accurate and explainable AI applications.

Free comprehensive course from Activeloop with 60+ lessons and 10+ practical projects, teaching production-ready LLM applications with vector databases, trusted by 10,000+ engineers.

IBM's comprehensive specialization providing job-ready vector database skills in one month, covering foundational knowledge for LLM-powered AI similarity searches, available for free enrollment.

A course focused on mastering multimodal Retrieval Augmented Generation (RAG) and embeddings, which are fundamental components often stored and managed by vector databases.

An advanced RAG architecture where an AI agent autonomously decides which questions to ask, which tools to use, when to retrieve information, and how to aggregate results. Represents a major trend in 2026 for more intelligent and adaptive retrieval systems.

The DataRobot vector databases feature provides FAISS-based internal vector databases and connections to external vector databases such as Pinecone, Elasticsearch, and Milvus. It supports creating and configuring vector databases, adding internal and external data sources, versioning internal and connected databases, and registering and deploying vector databases within the DataRobot AI platform to power retrieval-augmented generation and other AI use cases.